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Volume 45 Issue 2
Apr.  2025
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Article Navigation >  Diamond & Abrasives Engineering  > 2025  >  45(2): 266-273
ZHANG Feng, FENG Zhongli, XU Feng, ZHANG Deming, ZENG Xiangrui, MA Jianwei, ZHANG Shilei. Research on process optimization and trajectory planning of EA4T axle robot grinding[J]. Diamond & Abrasives Engineering, 2025, 45(2): 266-273. doi: 10.13394/j.cnki.jgszz.2024.0187
Citation: ZHANG Feng, FENG Zhongli, XU Feng, ZHANG Deming, ZENG Xiangrui, MA Jianwei, ZHANG Shilei. Research on process optimization and trajectory planning of EA4T axle robot grinding[J]. Diamond & Abrasives Engineering, 2025, 45(2): 266-273. doi: 10.13394/j.cnki.jgszz.2024.0187
shu

Research on process optimization and trajectory planning of EA4T axle robot grinding

doi: 10.13394/j.cnki.jgszz.2024.0187
  • 1.

    CRRC Qingdao Sifang Co., Ltd., Qingdao 266000, Shandong, China

  • 2.

    State Key Laboratory of High-Performance Precision Manufacturing, School of Mechanical Engineering,Dalian University of Technology, Dalian 116024, Liaoning, China

More Information
  • Received Date: 2024-11-30
  • Accepted Date: 2025-02-14
  • Rev Recd Date: 2025-01-18
    Abstract
  •   Objectives  The EA4T axle is a critical load-bearing component of electric multiple unit (EMU) train bodies, directly influencing operational safety and reliability. As a high-end product with stringent technical requirements and complex manufacturing processes, the shoulder position of the EA4T axle is stressed repeatedly and there is stress concentration during service. Consequently, in the process of axle production, it is necessary to grind the axle shoulder to control its surface roughness and material removal depth. Current manual grinding methods for EA4T axle shoulder suffer from high labor intensity, inconsistent surface quality, and low efficiency. In order to effectively break through the current manual grinding dilemma of the EMU EA4T axle, the implementation of flexible grinding using an industrial robotic intelligent grinding system equipped with a constant-force control device presents a feasible solution to replace manual operations and achieve automated processing. Therefore, it is essential to carry out research on the grinding process of EA4T steel components, and explore the grinding process methods that meet the surface quality requirements of EA4T axle machining. Combined with the off-line programming method for EA4T axle robot grinding trajectory, the axis shoulder grinding trajectory is planned and the robot machining program is generated to realize high-quality and efficient automatic grinding of the EA4T axle by robot.  Methods  Firstly, an independently developed robotic intelligent constant-force grinding system serves as the experimental platform. EA4T steel specimens with dimensions of 150 mm × 63 mm × 9 mm are prepared as test pieces. Based on the quality control requirement that the surface roughness of the EA4T axle after grinding must not exceed 0.4 μm, and considering the actual situation of manual grinding process parameters, a Taguchi method-based orthogonal experiment with four factors and four levels is designed and implemented. In the experiment, a hand-held surface roughness measuring instrument is used to measure the surface roughness after grinding, and a precision analytical balance is used to measure the weight of the specimen before and after grinding to calculate the material removal depth. Thus, the surface roughness and the material removal depth of the specimen under different process parameters are obtained. Secondly, analysis of variance and significance testing are conducted to determine the significance level of the influence of each process parameter on the experimental results. The influence of the grit size of grinding tools, grinding force, feed speed, and spindle speed on the surface roughness and material removal depth is analyzed. Then, by calculating the entropy of each index to determine the weight coefficient, the surface roughness and material removal depth in the experimental results of each group are converted into comprehensive score values for evaluation. The optimal grinding process parameter combination with minimum surface roughness and material removal depth is obtained through comprehensive score range analysis. Finally, the off-line programming method is employed to establish a virtual model of the robotic intelligent grinding system within the robot off-line programming software. The 3D model of the EA4T axle is imported into the virtual environment. Based on the flexible grinding module at the end-effector, parameters including grinding head dimensions, end-effector tools, and trajectory configurations are defined. The robot machining system program SRC file is generated and subsequently transferred to the robot teach pendant. The grinding force, feed rate and spindle speed corresponding to the optimal grinding process parameters are entered into the control system. Physical grinding experiments are conducted on EA4T axle prototypes to validate the feasibility of the proposed grinding methodology.  Results  Through the grinding orthogonal experiments and physical verification experiments, the following results are obtained. (1) The order of influence of grinding process parameters on the surface roughness of EA4T steel is: abrasive grit size > spindle speed > feed rate > grinding force, with abrasive grit size exhibiting the most significant impact on surface roughness. The order of influence of process parameters on material removal depth is spindle speed > abrasive grit size > feed rate > grinding force, with spindle speed being the most influential. (2) With the goal of minimizing the comprehensive score of surface roughness and material removal depth, the optimized grinding parameter combination is selected by choosing the levels with the lowest mean values across all parameter groups. The selected parameters are brasive grit size 400#, grinding force 15 N, feed rate 50 mm/s, and spindle speed 750 r/min. Using this parameter combination, the post-grinding surface roughness reaches 0.338 μm, and the material removal depth is 1.67 μm, effectively improving surface quality while meeting specification requirements. (3) The off-line programming method is used to plan the grinding trajectory. The simulation and experiment trajectories of EA4T robot grinding completely coincide, realizing automatic grinding robot of the EA4T axle shoulder position without interference, singularities and with full reachability.  Conclusions  The paper conducts experimental research on process optimization and trajectory planning for robotic intelligent grinding of the EA4T axle. Through orthogonal experiments combined with the entropy weight method, the influence patterns of grinding processes on quality are revealed. The optimal process parameter combination for minimizing surface roughness and material removal depth is determined. The off-line programming method enables quick and accurate planning of a robot grinding trajectory that is non-interfering, non-singular and fully reachable. The proposed method improves grinding efficiency and surface quality, meets the requirements of grinding efficiency and surface quality of EA4T axle, and can be applied in actual production and processing, effectively breaking through the predicament of low efficiency and poor consistency of EA4T axle.

     

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